Trimming blackhole clusters

ABSTRACT

Disclosed are techniques for trimming large clusters of related records. In one embodiment, a method is disclosed comprising receiving a set of clusters, each cluster in the clusters including a plurality of records. The method extracts an oversized cluster in the set of clusters and performs a breadth-first search (BFS) on the oversized cluster to generate a list of visited records. The method terminates the BFS upon determining that the size of the list of visited records exceeds a maximum size and generates a new cluster from the list of visited records and adding the new cluster to the set of clusters. By recursively performing BFS traverse over the oversized cluster and extracting smaller new clusters from it, the oversized cluster is eventually partitioned into a set of sub-clusters with the size smaller than the predefined threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is continuation of and claims the benefit of U.S.application Ser. No. 16/938,233, filed Jul. 24, 2020, which isincorporated by reference in its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentand the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever.

BACKGROUND

Currently, many organizations collect and store large amounts of datarecords in one or more databases. These data records may reflectcustomer information, business records, events, products, or otherrecords. These records can accumulate from a number of data sources. Forexample, a retail company may sell products over different channels suchas online e-commerce platforms as well as physical store locations. Theretail company may maintain separate customer records for each of itsdifferent retail channels.

Frequently, organizations attempt to synchronize many records. Forexample, organizations may attempt to group multiple records for asingle person or entity. Thus, a single person or entity can beassociated with multiple records, generated by multiple sources.However, current approaches suffer from numerous drawbacks.

Many data sources are inaccurate. For example, many retail channelscollect a customer's “address,” but this address is incorrectlyidentified as, for example, the store address. Such a situation oftenoccurs when data is collected at a point of sale, where a clerk or storeemployee enters a customer's data. In many instances, data is enteredsolely to complete a transaction and little if any attention is paid toits accuracy. As a result, multiple records having the same incorrectaddress may be stored in an organization's database. When anorganization attempts to cluster records around individual entities,this results in very large clusters due to “similar” data being sharedamong distinct users. Thus, while most users will have one or twoaddresses, and fewer users will share an address, there will existextremely large clusters of data associated with particular data points(e.g., the incorrectly entered store address). Thus, while a standardcluster for a user may include under ten records, such large clustersmay have millions of records allegedly associated with a single user.

To compensate for this, many organizations attempt to apply rudimentary“business rules” to break apart such large clusters. Such attemptsiterate through all pairs of records in a large cluster and attempt tofilter out errant connections. Another strategy is to apply a hierarchalclustering algorithm to a large cluster to generate multiplesub-clusters. Both of these approaches provide reasonable results at areasonable performance cost for small clusters (e.g., clusters havingless than a thousand records). However, these techniques break downsignificantly for large scale clusters. Across a large dataset,performing pairwise comparisons has a time complexity of O(n²), whilehierarchal clustering results has a time complexity of O(n³). Further,hierarchal clustering requires O(n²) memory and thus is infeasible formany large datasets.

BRIEF SUMMARY

The disclosed embodiments solve these and other problems by providing atechnique for quickly segmenting a large cluster (referred to as ablackhole cluster) into multiple, smaller sub-clusters. The disclosedembodiments improve both the time required to process such largeclusters but also reduce the memory needed to perform such operationswhen compared to existing techniques.

In one embodiment, a method is disclosed comprising receiving a set ofclusters, each cluster in the clusters including a plurality of records;extracting an oversized cluster in the set of clusters; performing abreadth-first search (BFS) on the oversized cluster, the BFS generatinga list of visited records; terminating the BFS upon determining that asize of the list of visited records exceeds a maximum size; andgenerating a new cluster from the list of visited records and adding thenew cluster to the set of clusters.

In another embodiment, a non-transitory computer-readable storage mediumfor tangibly storing computer program instructions capable of beingexecuted by a computer processor is disclosed, the computer programinstructions defining the steps of: receiving a set of clusters, eachcluster in the clusters including a plurality of records; extracting anoversized cluster in the set of clusters; performing a breadth-firstsearch (BFS) on the oversized cluster, the BFS generating a list ofvisited records; terminating the BFS upon determining that a size of thelist of visited records exceeds a maximum size; and generating a newcluster from the list of visited records and adding the new cluster tothe set of clusters.

In another embodiment, an apparatus is disclosed comprising: aprocessor; and a storage medium for tangibly storing thereon programlogic for execution by the processor, the stored program logic causingthe processor to perform the operations of receiving a set of clusters,each cluster in the clusters including a plurality of records;extracting an oversized cluster in the set of clusters; performing abreadth-first search (BFS) on the oversized cluster, the BFS generatinga list of visited records; terminating the BFS upon determining that asize of the list of visited records exceeds a maximum size; andgenerating a new cluster from the list of visited records and adding thenew cluster to the set of clusters.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the present disclosure can be better understood withreference to the attached drawings. The components in the drawings arenot necessarily drawn to scale, with emphasis instead being placed uponclearly illustrating the principles of the disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout several views.

FIG. 1 is a block diagram of a system for clustering records accordingto some embodiments of the disclosure.

FIG. 2 is a flow diagram illustrating a method for performinghierarchical clustering of data records with conflict resolutionaccording to some embodiments of the disclosure.

FIG. 3 is a flow diagram illustrating a method for de-clustering a largecluster according to some embodiments of the disclosure.

FIG. 4 is an example of a visual depiction of a hierarchical clusteringgenerated by a software application executed in a computing environmentaccording to some embodiments of the disclosure

FIGS. 5A through 5C illustrate a blackhole cluster before and afterde-clustering according to some embodiments.

FIG. 6 is a block diagram illustrating a computing system according tosome embodiments of the disclosure.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a system for clustering records accordingto some embodiments of the disclosure.

The system (100) includes a computing system (101) that is made up of acombination of hardware and software. The computing system (101)includes a database (103), a software application (106), and aclassifier (109). The computing system (101) may be connected to anetwork (102) such as the Internet, intranets, extranets, wide areanetworks (WANs), local area networks (LANs), wired networks, wirelessnetworks, or other suitable networks, etc., or any combination of two ormore such networks.

The computing system (101) may comprise, for example, a server computeror any other system providing computing capability. Alternatively, thecomputing system (101) may employ a plurality of computing devices thatmay be arranged, for example, in one or more server banks or computerbanks or other arrangements. Such computing devices may be located in asingle installation or may be distributed among many differentgeographical locations. For example, the computing system (101) mayinclude a plurality of computing devices that together may comprise ahosted computing resource, a grid computing resource, and/or any otherdistributed computing arrangement. In some cases, the computing system(101) may correspond to an elastic computing resource where the allottedcapacity of processing, network, storage, or other computing-relatedresources may vary over time. The computing system (101) may implementone or more virtual machines that use the resources of the computingsystem (101).

Various applications and/or other functionality may be executed in thecomputing system (101) according to various embodiments. Also, variousdata is stored in the database (103) or other memory that is accessibleto the computing system (101). The database (103) may represent one ormore databases (103).

The components executed on the computing system (101) include a softwareapplication (106) and a classifier (109), which may access the contentsof the database (103). According to various embodiments, the softwareapplication (106) is configured to generate hierarchical clusters usingconflict resolution as described in FIG. 2 and de-cluster oversizedclusters, as described in FIG. 3 . The computing system (101) employs aclassifier (109) that may be integrated into the software application(106) or may be implemented as a separate module, as illustrated. Insome embodiments, the classifier (109) may be configured as a softwareapplication as part of application software (106). In other embodiments,the classifier (109) may be configured as external software (e.g.,software running remote from application software, 106). In yet anotherembodiment, the classifier (109) may be implemented as a dedicatedhardware device (e.g., an ASIC, FPGA, or other device). The classifier(109) may be an ordinal classifier. According to various embodiments, anordinal classifier is a software component that receives two inputs andgenerates at least an ordinal label that reflects a degree of matchbetween the two inputs. For example, an ordinal label may include, butis not limited to, a “Strong-Match,” “Moderate-Match,” “Weak-Match,”“Unknown,” “Hard-Conflict.” An “Unknown” label represents a case wherethere is no match, and the compared data contains no hard conflict. A“Hard-Conflict” represents a case where there is no match, and thecompared data is inconsistent in a manner indicative of a hard conflict.Other user-defined ordinal labels may be used to express variousclassifications for performing hierarchical clustering or conflictresolution.

The data stored in the database (103) includes one or more databasetables (112). A database table (112) includes several records, whereeach record has one or more corresponding fields. When stored in arelational database (103), a database table (112) may be linked to oneor more relational tables (115). For example, if an airline companymaintained a database table (112) that stored customer records, theremay be a relational table (115) storing the flight history for eachcustomer. The contents of the relational table (115) link to acorresponding record using, for example, a record ID or foreign keyincluded in the table (112).

The software application (106) executing in the computing system (101)may generate a processed database table (118) by processing one or moredatabase tables (112). For example, the processed database table (118)may be a merged database table that is generated by de-duplicating atleast one database table (112). Thus, the processed database (118)includes information that allows one or more records to be consolidatedin the event they are deemed to be a match. According to variousembodiments of the disclosure, the degree of strength in a match isreflected in the merged database using, for example, a cluster ID.

According to various embodiments, the processed database table (118) isa relational database table that maintains the same relational links ofthe database tables (112) after it is processed.

The system (100) also includes one or more client device(s) (124). Aclient device (124) allows a user to interact with the components of thecomputing system (101) over a network (102). A client device (124) maybe, for example, a cell phone, laptop, personal computer, mobile device,or any other computing device used by a user. The client device (124)may include an application such as a web browser or mobile applicationthat communicates with the software application (106) to access,manipulate, edit, or otherwise process database tables (112). Thesoftware application (106) sends and receives information to and fromthe client device (124).

Next, a general description of the operation of the various componentsof the computing system (101) is provided. Various businesses or otherentities utilize the computing system to store information in a database(103). For example, businesses may want to store records reflectingcustomers, products, transactions, events, items, or any other piece ofinformation relevant to the business. Records are collected over timeand stored in one or more database tables (112). For example, when abusiness gets a new customer, a software program may create a recordreflecting the new customer. This record may include the customer'sname, address, contact information, or any other information thatidentifies the customer. Such information is stored as fields within adatabase table.

In practice, a single record is sufficient to represent a customer.However, it is possible that duplicate (e.g., redundant) records areinadvertently or unintentionally created and/or exist within one or moredatabases (103). For example, a customer may register with a businessvia an online portal, which creates a customer record for that customer.Later, the same customer may inadvertently register again with theonline portal, thereby creating a redundant customer record in thedatabase table (112). Also, a company may have a first database table(112) for its brick and mortar customers and a second database table(112) for its e-commerce customers. It is possible that the samecustomer has a corresponding record in these two different databasetables (112). As another example, two businesses maintaining their owncustomer records may merge such that the same customer may exist in twodifferent database tables (112). The resulting processed database tablecould have redundant records reflecting the same customer.

Duplicate records are not necessarily identical. While they possessoverlapping information, there may be field values that are different.For example, the field values of “Joe” and “Joseph” are not identical,yet they may be part of duplicate records. Because multiple records mayrepresent the same real-world entity, it is desirable to group relatedrecords together so that they are clustered. A classifier (109) may beused to determine whether two records should be classified as a matchbased on the degree of related or common field values between the tworecords. The classifier (109) may determine the likelihood that a pairof records represent the same real-world entity, such as, for example, aparticular customer. The classifier (109) may calculate a raw score thatquantifies the degree of similarity between two records. The raw scoremay be converted to a normalized score. An ordinal label may be assignedto the normalized score. An example of this is depicted in Table 1below, where a normalized score x is assigned an ordinal label if itfalls within a particular range:

TABLE 1 Normalized Score (x) Ordinal Label x ≤ 1 Hard-Conflict 1 < x ≤ 2Unknown 2 < x ≤ 3 Weak-Match 3 < x ≤ 4 Moderate-Match 4 < x ≤ 5Strong-Match

When performing a pairwise comparison of records, different combinationsof field values among the two records are compared. For example, in oneembodiment, the value of Field 1 of a first record is compared to thevalue of Field 1 of a second record, then the value of Field 2 of thefirst record is compared to the value of Field 2 of the second record,and so on. The comparison of two values yields a feature with respect tothe record pair. A feature is a programmed calculation taking as inputsM records and/or other data such as external metadata and returns anumeric value as output. The variable M=2 in the case of handling arecord pair. That numeric output may be, for example, a real valuebounded between 0 and 1, or a binary value with two distinct outputs, 0being considered “false” and one (1) being considered “true.” A featurescore is the specific output value generated by a feature for a givenset of records or record pair. A feature score refers to the degree thattwo field values are the same.

For example, comparing the first name field value of “Joseph” to thefirst name field value of “Joe” may yield a “first_name_match” featurehaving a feature score of 0.88 on a scale of 0 to 1, where 0 meansno-match and 1 means a perfect match. In other embodiments, the firstname feature may be a binary value of “true/T,” meaning match, or“false/F,” meaning no-match. In addition, features may be determinedbased on a combination of field values. Here, a feature may be“full_name_match,” which is a feature based on concatenating a firstname field value with a last name field value.

Features are combined to form a feature signature. The feature signaturequantifies the extent that a pair of records likely represent the samereal-world entity. As an example, a feature signature may be made up offeatures such as “first_name_match,” “last_name_match,”“full_name_match,” “email_address_match,” etc. A feature signatureresulting from a pairwise comparison is inputted into a classifier (109)to determine an ordinal label for the two inputs.

While the description above discusses pairwise comparisons between tworecords, hierarchical clustering, according to various embodiments,performs pairwise comparisons between inputs that may be clusters ofrecords. A cluster may refer to a group of two or more records as wellas a single record. A cluster of one record is referred to as asingleton cluster. For example, a pairwise comparison may compare onesingleton cluster (a first input) to a cluster of multiple records (asecond input). As described in further detail below, using clusters asinputs to a classifier (109) provides hierarchical clustering.

FIG. 2 is a flow diagram illustrating a method for performinghierarchical clustering of data records with conflict resolutionaccording to some embodiments of the disclosure. The flow diagram ofFIG. 2 provides an example of the many different types of functionalarrangements that may be employed to implement the operation of theportion of the software application (106) as described herein. As analternative, the flow diagram of FIG. 2 may be viewed as depicting anexample of elements of a method implemented in the computing system(101) of FIG. 1 according to one or more embodiments.

In step 202, the method (200) accesses a database. In one embodiment,the method (200) accesses a database such as that depicted in FIG. 1 .In some embodiments, the database comprises a relational database, whilein other embodiments, the database may comprise any type of data storagedevice. In one embodiment, the method (200) remotely accesses thedatabase while, in others, the method (200) accesses a local database.

In step 204, the method (200) obtains one or more database tables fromthe database. For example, the method (200) may download one or moredatabase tables to local memory or cache. In other embodiments, themethod (200) may present credentials to gain permission to access one ormore database tables. In some embodiments, the one or more databasetables include records that are subject to hierarchical clustering.

In step 206, if there are multiple database tables, method (200)preprocesses the database tables. For example, the method (200) may fuseor otherwise concatenate the database tables in a manner described inco-pending U.S. patent application Ser. No. 15/729,931, which is titled,“Effectively Fusing Database Tables” and which is incorporated byreference in its entirety.

In step 208, the method (200) performs one or more blocking operations.In the illustrated embodiment, a blocking operation is used to identifya block of records among the one or more database tables that are likelyto refer to the same real-world entity. A blocking operation may providea rough estimate of what records should be clustered together. Ablocking operation may use a blocking rule that is based on whether therecords across one or more database tables contain an exact match withrespect to at least one field in the at least one database table. Forexample, a blocking rule may check whether there exists a first name andlast name match across all records. This may form a block of records,which serves as a starting point for performing hierarchical clustering.

According to various embodiments, different blocking operations areperformed before performing hierarchical clustering. Records in one ormore database tables may be blocked according to a first blockingoperation such as a name match rule and a second blocking operation suchas an email match rule. Thus, a record within a database table may beassociated with one or more blocks.

In some embodiments, the blocking operations may additionally include afiltering operation that excludes records that are clearly not related.In one embodiment, the filtering may include identifying a set of fieldsthat can be used to exclude records. While in some scenarios, a singlefield may be used, multiple fields generally provide better accuracy.For example, a user's phone number, identified gender, and location maybe used to “rule out” records as matching a single user. Notably, whenusing a single record (e.g., phone number, or gender), it is possiblethat a user may change such a field during the course of their life;thus, multiple fields are used to confirm an exclusion. In otherembodiments, a timestamp may be used to filter records. For example, twosimilar records for “Jane Doe” may be present at the same time (orwithin a short time window) but have fields having vastly differentvalue (e.g., home addresses in different continents). While some usersmay have addresses in varying locations, most users will utilize thesame home address; thus, the combination of a timestamp (or window) andone or more fields may be used to filter clearly non-matching users.

In step 210, the method (200) performs pairwise comparisons andclassifications for a given block of records. In one embodiment, themethod (200) invokes a classifier to classify various input records. Themethod (200) performs pairwise comparisons and classifications on allrecord pairs within each block of records.

The pairwise comparisons identify a match status for record pairs withina set of records. The set of records may be organized into connectedcomponents by positive edges. A positive edge refers to a matched recordpair or otherwise a record pair having a match status higher than athreshold level. A connected component refers to a subset of recordsderived from the records in one or more database tables. This isdiscussed in further detail below.

In one embodiment, the method (200) selects a pair of records to performa pairwise comparison. A pair of records may be selected from a clustermade up of multiple records or a singleton cluster. The first input andsecond input in the pair of records may be selected by the method (200)according to a hierarchical clustering algorithm that iterativelyselects inputs, as discussed in various embodiments below. Once the twoinputs are selected, the method (200) performs a pairwise comparison. Inone embodiment, a pairwise comparison comprises comparing the fieldvalues between the first input and second input to determine a featurefor a particular field or set of fields. The pairwise comparisongenerates a feature signature which may be made up of various featuresof the fields' values being compared. The feature signature reflects howtwo inputs are similar or dissimilar based on the extent the fieldvalues are similar. In other words, the feature signature corresponds toa series of features between a pair of inputs being compared. A firstpair of inputs may have the same feature signature as a different set ofinputs even though the first pair represents a different entity than thesecond pair. In this case, it is inferred that the first pair of inputsare similar to each other in the same way that the second pair of inputsare similar to one another. For example, given the trivial set offeatures “Fuzzy Last Name match” and “Fuzzy First Name match”, the firstpair of inputs {“Stephen Meyles”, “Steve Myles”} will generate a featuresignature of [1 1], where “1” refers to a binary value indicating amatch. In addition, a second pair of inputs {“Derek Slager”, “DerkeSlagr”} will also generate a feature signature of [1 1]. This does notnecessarily mean that the first pair of inputs are related to the samereal-world identity as the second pair of inputs. Instead, it suggeststhat the inputs have the same data variations (fuzzy matches of firstand last name). Records with the same data variations will have the samesignature.

After generating the feature signature, the method (200) may use aclassifier to perform a classification of the feature signature. Thisclassification process calculates a raw score that correlates to thestrength that a particular feature signature indicates a match. The rawscore may be any range of numbers. The raw score quantifies theconfidence that a particular feature signature represents two inputsthat refer to the same real-world entity. In some embodiments, the rawscore may be normalized to a normalized score. In addition, an ordinallabel may be assigned to the raw score or normalized score, as discussedabove. To elaborate further, after calculating raw score or normalizedscore, the software application compares the raw score or normalizedscore to predetermined threshold ranges to yield a corresponding ordinallabel that classifies the feature signature.

According to various embodiments, the classifier is configured usingordinal training data and/or hard conflict rules. Ordinal training datais generated from users who manually label test data to build businesslogic (e.g., a history) of how people would classify two inputs. Theclassifier is “trained” in the sense that it applies ordinal training229 and extrapolates it and applies it to new combinations of inputpairs. For example, if the ordinal training data indicates that aparticular feature was repeatedly labeled as a “Moderate-Match” among aplurality of other labels, then the classifier will generate a raw scorethat corresponds to the ordinal label of “moderate-match.”

According to various embodiments, the classifier can classify a pair ofrecords or a pair of clusters. The classifier allows each field to be avector, for example, an “email” field may be [“r-1 @test-one.com” “r-1@test-two.com”]. When applying the classifier to cluster pairs, eachsemantic field is a concatenation of the semantic values from eachcluster member. For example, a first input made of two records, R-1 andR-2, may have email address values of “email-r-1@test.com” and“email-r-2@test.com”, respectively. The email field for this clusterbecomes [“email-r-1@test.com”, “email-r-2 test.com”].

When configured to apply hard conflict rules, the classifier may analyzethe feature signature or the input pair and check whether a rule isviolated. An example of a hard conflict rule is whether the field valuesfor a “social security number” field is an exact match. If it is not anexact match, the classifier will apply an ordinal label of“Hard-Conflict” regardless of the remainder of the feature signature. Ifthere are real-world scenarios where two records should never beclustered, it is appropriate to apply a hard conflict rule.

In step 212, the method (200) determines a subset of records such as,for example, a connected component based on the positive edges from theclassification results. The concept of a connected component refers togrouping records together based on whether there is a sufficientlystrong match between records pairs and by applying transitiveassociation. For example, If R1 and R2 have a match and R2 and R3 have amatch, then the method (200) connects R1, R2, and R3 through transitiveassociation. In some embodiments, the method (200) applies a union-findalgorithm to build a transitive closure representing the connectedcomponent. In this manner, the method (200) determines a connectedcomponent (e.g., R1, R2, and R3) which is a set of connected recordswithin the blocks.

To elaborate further, the method (200) collects the positive recordpairs (record pairs with the classifier score higher than thepre-specified threshold). After that, connected components arealgorithmically constructed from the positive record pairs. In eachconnected component, every record is connected with others through oneor more positive edges (to the extent one exists) directly ortransitively. The method (200) continues across different connectedcomponents until there is no positive edge left.

Since records are allowed to be connected through transitivity insidethe connected component, sometimes hard conflicts will occur, andsometimes not. Each connected component becomes the input of thehierarchical clustering algorithm. As discussed below, hierarchicalclustering is applied to each connected component to further partitionthe component and resolve any hard-conflict it detects.

In step 214, after generating a plurality of connected components, themethod (200) de-clusters any large connected components. Details of thissub-routine are provided in more detail in FIG. 3 and are not repeatedherein.

In step 216, the method (200) generates hierarchical clusters for agiven connected component. Hierarchical clusters may be stored as akey-value database as the hierarchical clusters are being generated by asoftware application. In step 218, the method (200) generates aprocessed database table. In some embodiments, the method (200)generates hierarchical cluster IDs for each record and assigns them tothe records in the processed database table.

In one embodiment, the method (200) derives a connected component fromone or more database tables. To derive or otherwise determine aconnected component, records may be identified in response to firstperforming a blocking operation. Then, the method (200) performs aclassification to identify positive record pairs; and lastly, the method(200) connects them together to form a connected component. Next, themethod (200) may treat each record as a singleton cluster. In thisrespect, the pairwise comparisons are subsequently performed on twoinputs, the inputs comprising a pair of singleton clusters.

In some embodiments, the method (200) further performs pairwisecomparisons and classifications on remaining clusters to generatecorresponding match scores as part of step 516. For the first iteration,the pairwise comparisons are performed on the various combinations ofrecord pairs in the connected component. If a pairwise comparison waspreviously performed on a particular records pair, then the method (200)applies the result of that previously performed comparison withoutperforming a redundant calculation. Because pairwise comparisons wereperformed to create the connected component, the results of theseprevious pairwise comparisons are stored and reused for future purposesof hierarchical clustering.

In some embodiments, the method (200) further removes hard conflictsfrom consideration as part of step 516. For example, the method (200)can look for two inputs where a hard conflict arises. The method (200)records instances of two inputs having a hard conflict to ensure thatsubsequent iterations of clustering will avoid clustering together thosetwo inputs. Using ordinal classification and applying a “Hard-Conflict”label allows the software application to detect hard conflicts. Also,the software application may implement one or more hard conflict rulesto screen for hard conflicts without classification. The method (200)may also identify the highest score above a minimum threshold. Here, themethod (200) searches for the strongest match among the remainingclusters. The minimum threshold may be the lowest threshold for anacceptable match, such as a weak-match. Thus, the method (200) continuesto iterate as long as there is at least a weak-match in the remainingclusters of the connected component.

In some embodiments, the method (200) further merges clusters using thehighest match score as part of step 516. The inputs having the highestmatch score are merged into a single cluster. The inputs may besingleton clusters or multi-record clusters. This marks the completionof an iteration. Thereafter, the method (200) performs a subsequentiteration. In a subsequent iteration, the remaining clusters includesome initial or intermediate cluster that was generated from a previousiteration.

In some embodiments, when there are no inputs having a sufficiently highmatch score, the method (200) assigns hierarchical cluster IDs toremaining clusters as part of step 516. As the method (200) iteratesthrough performing pairwise comparisons and classifications, itgenerates hierarchical clustering, where each tier corresponds to athreshold match score. These threshold match scores may correspond tothe ordinal labels assigned to various input pairs. Thus, the method(200) generates hierarchical cluster IDs according to the hierarchicalclustering.

The following is an example of applying the flowchart of FIG. 2 to adatabase table made up of at least records R_(A), R_(B), R_(C), andR_(D). At step 202, one or more database tables are accessed to obtainrecords R_(A) through R_(D). The database tables are processed at step206 to normalize records R_(A) through R_(D) so that their field valuesmay be compared. At step 208, two blocking operations are performed onthe processed database table. A first blocking operation blocks recordsaccording to name while a second blocking operation blocks recordsaccording to zip code. As a result, R_(A), R_(B), and R_(C) form a firstblock while R_(A) and R_(D) form a second block. At step 210, thevarious record pairs of the first block ({R_(A), R_(B)}, {R_(A), R_(D)},{R_(B), R_(C)}) are compared and classified. The same applies to thesecond block where R_(A) is compared to R_(D). The record pairs {R_(A),R_(B)} and {R_(A), R_(D)} have match labels that exceed a thresholdlevel. For example, they may correspond to a Strong-Match,Moderate-Match, or Weak-Match or any other status that indicates aminimum status for a match. The other records pairs, {R_(B)-R_(C)} and{R_(A)-R_(C)} correspond to a low match status, an unknown match statusor a hard conflict, below the threshold level established as a minimumstatus for a match. At step 212, a connected component is created fromR_(A), R_(B), and R_(A). The connected component is derived from thevarious records in the database by blocking, classifying, and evaluatingmatch scores. The connected component of R_(A), R_(B), and R_(D) removesrecords having a hard conflict or records with sufficiently low matchstatuses. In one embodiment, the connected component may include morethan three records. Indeed, in some embodiments, the connected componentmay have millions of records. In this scenario, in step 214, the method(200) would de-cluster the components exceeding a pre-configured maximumsize. In one embodiment, the maximum size comprises a maximum number ofrecords allowed in a cluster and may be configured based on the needs ofthe system. Since the exemplary cluster includes only three records,step 214 is skipped. At step 216, the connected component is subject tohierarchical clustering. Finally, in step 218, the database is populatedwith the hierarchal clustering results.

FIG. 3 is a flow diagram illustrating a method for de-clustering a largecluster according to some embodiments of the disclosure.

In step 302, the method (300) selects a cluster. In the illustratedembodiment, the cluster selected in step 301 comprises a componentgenerated in step 210 of the method (200) depicted in FIG. 2 . Ingeneral, each cluster comprises a plurality of records and a pluralityof edges or connections between these records.

In step 304, the method (300) determines if the number of records in theselected cluster exceeds a pre-configured threshold. In someembodiments, this threshold is a static value (e.g., 1000). The specificvalue used may be tuned based on the system's needs, and the disclosureis not limited to any specific value for the threshold. In someembodiments, the threshold may be dynamically or functionallydetermined. For example, in some embodiments, the threshold may becomputed as a function of the total number of records in all clusters.For example, if the total number of records in the cluster is n, thethreshold may be defined as c+log(n)^(b), where c and b are tunableparameters whereby c defines a minimum threshold and log(n)^(b)increases the threshold as the total size n of the record spaceincreases. The specific formula is not intended to be limiting.

If the method (300) determines that the cluster size is below thepre-configured threshold, the method (300) then determines if anyclusters remain to be analyzed in step 306. If so, the method (300)re-executes steps 302 and 304 for each remaining cluster. If not, themethod (300) ends.

In some embodiments, the method (300) steps through all clustersgenerated in step 212 of FIG. 2 . In an alternative embodiment, themethod (200) may record the size of clusters as they are beinggenerated. In this scenario, the method (200) will execute the method(300) only on those clusters exceeding the pre-configured threshold.Thus, the method (300) can bypass step 304. As will be discussed, themethod (300) executes steps 308 through 318 through all clustersexceeding the pre-configured size threshold.

In step 308, the method (300) ranks the connectedness (i.e., the numberof connections) of the records in the cluster. As described in FIG. 2 ,each cluster includes a set of records and a plurality of edges betweenthe records that represent pairwise connections between the records.Thus, each record has at least one edge. The method (300), in step 308,may access a database table that records the edge data. In someembodiments, the database table stores a record and a list of connectedrecords. Thus, in some embodiments, the method (300) can simply sortthis table using a sorting algorithm such as quicksort, merge sort, heapsort, etc. The specific algorithm is not limited but can be chosen toreduce the overall computational complexity of the process.

In step 310, the method (300) selects the highest-connected record. Thehighest-connected record refers to a record connected to the mostrecords. As described, this element will comprise the highest rank itemgenerated in step 308. In some scenarios, a single record will comprisethe highest-connected record. In other scenarios, however, multiplerecords may have the same degree. In this scenario, the method (300) canarbitrarily choose a record. However, in some embodiments, the method(300) may use a degree weighting to select the highest-connected record.That is, as described above, edges between records may be weighted basedon their strength. Thus, a record having three (total) strong edges maybe selected as the highest-connected record compared to a record havingthree (total) weak edges.

In step 312, the method (300) performs a breadth-first search (BFS) onthe cluster. In the illustrated embodiment, the method (300) starts theBFS at the highest-connected record selected in step 310. In theillustrated embodiment, when performing a BFS, the method (300)maintains an array of “visited” records as the BFS algorithm walksthrough the graph. As will be discussed, this list of visited recordswill be used to segment the graph upon determining that a maximum sizeis met. In some embodiments, an array, list, or other data structure isused to maintain the visited records.

In step 314, the method (300) determines if a maximum size is met. Insome embodiments, the maximum size is equal to the pre-configuredthreshold. In step 312, the method (300) traverses the cluster, and themethod (300) simultaneously records the records visited during the BFS.In the illustrated embodiment, the method (300) monitors the visitedrecords to determine when the number of visited records hits a maximumsize. For example, the method (300) may query the size of the array orlist storing the visited records as it performs the BFS to determine ifthe maximum size is reached.

In some embodiments, this maximum size is a fixed value (e.g., 1000). Inother embodiments, the maximum size may be specified by the operator ofthe method (300) and thus is an input parameter to the method (300). Inyet another embodiment, the maximum size may be computed based on thesize of the cluster. This computation may be done similarly to thatdescribed above regarding the triggering threshold that initiates themethod (300).

If the method (300) determines that the maximum size has not yet beenmet, the method (300) continues to traverse the cluster using the BFSalgorithm in step 312. If the method (300) determines that the maximumsize was met, the method (300) proceeds to step 314.

In one embodiment, the method (300) may perform a further check as partof steps 312 and 314. Specifically, this check may comprise limiting thedepth of the BFS. In some embodiments, this limit can be applied to thealgorithm in step 312. For example, the method (300) may specify thatthe BFS algorithm should not exceed a depth from the highest-connectedcluster of four (4). This limit reduces the likelihood of moreirrelevant records being returned as part of the cluster centered aroundthe highest-connected cluster. In some embodiments, this depth can bespecified by the operator of the method (300).

In step 316, the method (300) removes the visited records from thecluster. In the illustrated embodiment, the method (300) removes all ofthe records in the data structure that stores the visited records fromthe original cluster. As part of this process, any edges between avisited and non-visited node are removed from the original cluster.Similarly, these same edges are removed from the visited nodes. As aresult, after step 316, the method (300) will obtain two clusters: onecomprising the original cluster less the visited nodes and the othercomprising the visited nodes.

In step 318, the method (300) adds the visited records to the list ofclusters generated in step 212. In this manner, the method (300)“updates” the clusters of records based on the “de-clustering” performedvia the BFS. As a result, the method (200) receives a set of reasonablysized clusters that excludes any oversized or blackhole clusters.

In the illustrated embodiment, after executing step 318, the method(300) returns to step 304 to determine whether the size of the clusterexceeds the maximum size threshold. In each iteration, the clusteranalyzed in step 304 will be smaller, the previous cluster size less thevisited records removed in step 316.

In a simplistic scenario, the cutting of the cluster in step 316 willresult in two clusters: the visited cluster and the remaining cluster.In this scenario, the method (300) then performs steps 304 through 318on the remaining cluster.

In more common scenarios, the result of the cutting in step 316 willresult in a cluster of visited records (e.g., 505) and a remainingcluster that includes multiple unvisited records (as illustrated inFIGS. 5A through 5C), the unvisited records potentially representingadditional clusters once visited. In this scenario, steps 304 through318 can be parallelized to execute simultaneously on each cluster, thusimproving the speed of the operation. That is, if there are tworemaining clusters, steps 304 through 318 can be executed in parallelfor each remaining cluster.

Ultimately, the method (300) will determine that no remaining clusterexceeds the maximum size. Alternatively, the method (300) may simplytimeout. In either scenario, the method (300) has generated multiple newclusters that are smaller or equal to the maximum size and ends.

FIG. 4 is an example of a visual depiction of a hierarchical clusteringgenerated by a software application executed in a computing environment(such as that depicted in FIG. 1 ) according to some embodiments of thedisclosure.

In this example, there are seven records R1-R7. These records may havebeen identified from one or more database tables and determining aconnected component. Records R1-R7 represent records that have alikelihood of referring to the same entity such as a particular customeraccount.

A cluster of multiple records is depicted as a cloud bubble drawn aroundmultiple records. Single records form singleton clusters. The strengthof a match between two records is depicted by one or more lines betweentwo records. Stronger matches are depicted with more lines while weakermatches are depicted with fewer lines. For example, R1 and R3 form astrong match, as depicted with three (3) lines while R2 and R4 depict aweaker match with one line.

The hierarchical clustering in this example is made up of multiple tierswhere a bottom tier (401) applies a lower confidence matching, a middletier (402), applies a moderate confidence matching, and an upper tier(403) applies a higher confidence matching. When a lower confidencematching scheme is applied, the software application is configured tocluster records that have a relatively weaker link. Accordingly, thismay yield fewer clusters that are generally larger in size.

In the lower tier (401), the lower confidence matching yields a firstcluster (409 a) made up of records R1-R5, a second cluster (409 b) madeup of record R6 and a third cluster (409 c) made up of record R7. Withinthe tier, the seven records R1-R7 have been consolidated into threegroups or clusters. Consolidating records can lead to downstreamprocessing efficiency depending on how the end user wishes to use therecords. However, the tradeoff is that the clustering may include weakermatches.

In the middle tier (402), the moderate confidence matching yields afirst cluster (411 a) made up of records R1-R4, a second cluster (411 b)made up of record R5, a third cluster (411 c) made up of record R6, anda fourth cluster (411 d) made up of record R7. Within the tier, theseven records R1-R7 have been consolidated into four clusters. Whencompared to a lower tier (401), the moderate tier (402) has moreclusters, where the cluster size is smaller. For example, the firstcluster (409 a) of the lower tier (401) is split into two clusters (411a) and (411 b) in the middle tier (402). Under the moderate matchingscheme of the middle tier (402), weaker links, such as the link betweenR4 and R5 are not permitted to exist within a cluster.

In the upper tier (403), the higher confidence matching yields a firstcluster (413 a) made up of records R1-R3, a second cluster (413 b) madeup of record R4, a third cluster (413 c) made up of record R5, a fourthcluster (413 d) made up of record R6, and a fifth cluster (413 e) madeup of record R7. Within the tier, the seven records R1-R7 have beenconsolidated into five clusters. When compared to a lower tier (401) andmoderate tier (402), the upper-tier has more clusters, where the clustersize is smaller. Under the upper tier, only strong matches are permittedwhen forming clusters.

According to various embodiments, the software application connectsvarious records across different tiers (401, 402, 403) using a key-valuedatabase. A processed database table may be generated from the key-valuedatabase. In some embodiments of the above process, one or more of theclusters (409 a, 411 a, 413 a) may be oversized. In some embodiments, anoversized cluster (or, blackhole cluster) comprises a cluster having anumber of nodes or records that exceeds a pre-configured threshold. Inresponse to detecting such blackhole clusters, the method (300)described in FIG. 3 may be executed on each of these clusters. FIGS. 5Athrough 5C illustrate the operation of this method on an exampleblackhole cluster.

FIGS. 5A through 5C illustrate a blackhole cluster before and afterde-clustering according to some embodiments.

In FIG. 5A, a cluster (500 a) (e.g., connected component) is depictedcontaining 21 records (R1-R21). The cluster (500 a) depicted in FIG. 5Amay be generated in the manners described in FIGS. 1 and 4 . In theillustrated embodiment, the cluster (500 a) is oversized since itexceeds a pre-configured threshold. In this embodiment, thepre-configured threshold is four records, but the specific numericalvalue of the threshold is not limited. As one example, a productiondeployment may have a pre-configured threshold in the hundreds orthousands of records. The specific threshold may be tuned as the systemoperates.

As a first stage, the system ranks each of the nodes (R1-R21) in orderof its connectedness with other nodes (e.g., each node's degree). Thefollowing table illustrates the degrees of each node:

TABLE 2 Node Degree R1 4 R3, R4, R6, R9, R15, R19 3 R2, R5, R7, R8, R11,R14 2 R10, R12, R13, R16, R17, R18, R20, R21 1

Since R1 has the highest degree, it is selected as the root or seed of aBFS traversal. Examples of handling ties in degree are providedpreviously.

In the illustrated embodiment, the nodes (R1-R21) cluster (500 a) arenumbered to order the BFS traversal sequence. That is, a BFS routinewill first visit R1, R2, R3, etc. In the illustrated embodiment, themaximum size is set to equal the pre-configured threshold (4). Thus, theBFS accesses, R1, R2, R3, and R4 before the system halts the BFS sincethe maximum size was reached. These records (R1-R4) are then removedfrom the cluster and added as a new cluster.

FIG. 5B illustrates the state of the cluster after removing the firstset of visited nodes. As illustrated, visited nodes (505) are removedfrom the cluster, and five sub-clusters (501, 502, 503, 504, 506) areformed due to the cut. As illustrated, four of the sub-clusters (501,502, 503, 504) are below the maximum size (4) and thus can be added as“new” clusters and removed from the cluster (500 a). In someembodiments, the system may specify a minimum cluster size. In thisembodiment, the system may completely discard clusters that do not meetthe minimum size. For example, cluster (507) includes a single record(R10). In some embodiments, the system will discard this type of clustersince it provides no meaningful information. The specific minimum sizeis not limited in the disclosure. In an alternative embodiment, thesystem may not specify a minimum cluster size. In this embodiment, thesystem will retain all clusters, including small clusters.

In contrast, cluster (511) still exceeds the maximum size of (4), thusthe system will re-execute steps 304-318 of the method (300) of thiscluster (511). As described above, the system will first identify thehighest-connected record in cluster (511). In the illustratedembodiment, the system recomputes the ranking based on the new cluster,the results of which are listed below:

TABLE 3 Node Degree R15, R19 3 R9 2 R16, R18, R20, R21 1

As illustrated, nodes R15 and R19 have equal degree. As describedpreviously, the weight of this degree may be used to break the tie.Alternatively, if the two nodes are equal, the system may randomlyselect a node. In the illustrated embodiment, the system selects R15 andproceeds to perform a second BFS starting at R15. During the BFS, thesystem visits R15, R18, R9, and R19 before the BFS is terminated sincethe maximum size is reached.

As illustrated in FIG. 5C, records R15, R18, R9, and R19 are used as thesecond new cluster (513), while the remaining records are cut into theirown clusters (515, 517, 519). As described above, the system may addeach of the clusters (513, 515, 517) to a set of all clusters or mayoptionally discard smaller clusters (515, 517, 519).

FIG. 6 is a block diagram illustrating a computing system according tosome embodiments of the disclosure.

The computing system (101) includes one or more computing devices (600).Each computing device (600) includes at least one processor circuit, forexample, having a processor (603) and memory (606), both of which arecoupled to a local interface (609) or bus. To this end, each computingdevice (600) may comprise, for example, at least one server computer orlike device. The local interface (609) may comprise, for example, a databus with an accompanying address/control bus or other bus structure ascan be appreciated.

Stored in the memory (606) are both data and several components that areexecutable by the processor (603). In particular, stored in the memory(606) and executable by the processor (603) is the software application(106) and classifier (109). Also stored in the memory (606) may be adatabase (103) and other data such as, for example a one or moredatabase tables (112) and a processed database table (118). In addition,an operating system may be stored in the memory (606) and executable bythe processor (603).

It is understood that there may be other applications that are stored inthe memory (606) and are executable by the processor (603) as can beappreciated. Where any component discussed herein is implemented in theform of software, any one of a number of programming languages may beemployed, such as, for example, C, C++, C #, Objective C, Swift, Java®,JavaScript®, Perl, PHP, Visual Basic®, Python®, Ruby, Clojure, Flash®,or other programming languages.

Several software components are stored in the memory (606) and areexecutable by the processor (603). In this respect, the term“executable” means a program file that is in a form that can ultimatelybe run by the processor (603). Examples of executable programs may be,for example, a compiled program that can be translated into machine codein a format that can be loaded into a random access portion of thememory (606) and run by the processor (603), source code that may beexpressed in proper format such as object code that is capable of beingloaded into a random access portion of the memory (606) and executed bythe processor (603), or source code that may be interpreted by anotherexecutable program to generate instructions in a random-access portionof the memory (606) to be executed by the processor (603), etc. Anexecutable program may be stored in any portion or component of thememory (606) including, for example, random access memory (RAM),read-only memory (ROM), hard drive, solid-state drive, USB flash drive,memory card, optical disc such as compact disc (CD) or digital versatiledisc (DVD), floppy disk, magnetic tape, or other memory components.

The memory (606) is defined herein as including both volatile andnonvolatile memory and data storage components. Volatile components arethose that do not retain data values upon loss of power. Nonvolatilecomponents are those that retain data upon a loss of power. Thus, thememory (606) may comprise, for example, random access memory (RAM),read-only memory (ROM), hard disk drives, solid-state drives, USB flashdrives, memory cards accessed via a memory card reader, floppy disksaccessed via an associated floppy disk drive, optical discs accessed viaan optical disc drive, magnetic tapes accessed via an appropriate tapedrive, and/or other memory components, or a combination of any two ormore of these memory components. In addition, the RAM may comprise, forexample, static random-access memory (SRAM), dynamic random-accessmemory (DRAM), or magnetic random-access memory (MRAM) and other suchdevices. The ROM may comprise, for example, a programmable read-onlymemory (PROM), an erasable programmable read-only memory (EPROM), anelectrically erasable programmable read-only memory (EEPROM), or otherlike memory device.

Also, the processor (603) may represent multiple processors (603) and/ormultiple processor cores and the memory (606) may represent multiplememories (606) that operate in parallel processing circuits,respectively. In such a case, the local interface (609) may be anappropriate network that facilitates communication between any two ofthe multiple processors (603), between any processor (603) and any ofthe memories (606), or between any two of the memories (606), etc. Thelocal interface (609) may comprise additional systems designed tocoordinate this communication, including, for example, performing loadbalancing. The processor (603) may be of electrical or of some otheravailable construction.

Although the software application (106) described herein may be embodiedin software or code executed by general-purpose hardware as discussedabove, as an alternative the same may also be embodied in dedicatedhardware or a combination of software/general purpose hardware anddedicated hardware. If embodied in dedicated hardware, each can beimplemented as a circuit or state machine that employs any one of or acombination of a number of technologies. These technologies may include,but are not limited to, discrete logic circuits having logic gates forimplementing various logic functions upon an application of one or moredata signals, application specific integrated circuits (ASICs) havingappropriate logic gates, field-programmable gate arrays (FPGAs), orother components, etc. Such technologies are generally well known bythose skilled in the art and, consequently, are not described in detailherein.

The flowcharts discussed above show the functionality and operation ofan implementation of the software application (106). If embodied insoftware, each box may represent a module, segment, or portion of codethat comprises program instructions to implement the specified logicalfunction(s). The program instructions may be embodied in the form ofsource code that comprises human-readable statements written in aprogramming language or machine code that comprises numericalinstructions recognizable by a suitable execution system, such as aprocessor (603) in a computer system or other system. The machine codemay be converted from the source code, etc. If embodied in hardware,each block may represent a circuit or a number of interconnectedcircuits to implement the specified logical function(s).

Although the flowcharts show a specific order of execution, it isunderstood that the order of execution may differ from that which isdepicted. For example, the order of execution of two or more boxes maybe scrambled relative to the order shown. Also, two or more boxes shownin succession may be executed concurrently or with partial concurrence.Further, in some embodiments, one or more of the boxes may be skipped oromitted. In addition, any number of counters, state variables, warningsemaphores, or messages might be added to the logical flow describedherein, for purposes of enhanced utility, accounting, performancemeasurement, or providing troubleshooting aids, etc. It is understoodthat all such variations are within the scope of the present disclosure.

The software application (106) may also comprise software or code thatcan be embodied in any non-transitory computer-readable medium for useby or in connection with an instruction execution system such as, forexample, a processor (603) in a computer system or other system. In thissense, the logic may comprise, for example, statements includinginstructions and declarations that can be fetched from thecomputer-readable medium and executed by the instruction executionsystem. In the context of the present disclosure, a “computer-readablemedium” can be any medium that can contain, store, or maintain the logicor application described herein for use by or in connection with theinstruction execution system.

The computer-readable medium can comprise any one of many physical mediasuch as, for example, magnetic, optical, or semiconductor media. Morespecific examples of a suitable computer-readable medium would include,but are not limited to, magnetic tapes, magnetic floppy diskettes,magnetic hard drives, memory cards, solid-state drives, USB flashdrives, or optical discs. Also, the computer-readable medium may be arandom-access memory (RAM) including, for example, static random-accessmemory (SRAM) and dynamic random-access memory (DRAM), or magneticrandom-access memory (MRAM). In addition, the computer-readable mediummay be a read-only memory (ROM), a programmable read-only memory (PROM),an erasable programmable read-only memory (EPROM), an electricallyerasable programmable read-only memory (EEPROM), or other type of memorydevice.

Further, any logic or application described herein, including softwareapplication (106), may be implemented and structured in a variety ofways. For example, one or more applications described may be implementedas modules or components of a single application. Further, one or moreapplications described herein may be executed in shared or separatecomputing devices or a combination thereof. For example, the softwareapplication described herein may execute in the same computing device(600), or in multiple computing devices in the same computing system(101). Additionally, it is understood that terms such as “application,”“service,” “system,” “engine,” “module,” and so on may beinterchangeable and are not intended to be limiting.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to present that an item, term, etc., may beeither X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z).Thus, such disjunctive language is not generally intended to, and shouldnot, imply that certain embodiments require at least one of X, at leastone of Y, or at least one of Z to each be present.

It should be emphasized that the above-described embodiments of thepresent disclosure are merely possible examples of implementations setforth for a clear understanding of the principles of the disclosure.Many variations and modifications may be made to the above-describedembodiment(s) without departing substantially from the spirit andprinciples of the disclosure. All such modifications and variations areintended to be included herein within the scope of this disclosure andprotected by the following claims.

What is claimed is:
 1. A method comprising: receiving an oversizedcluster associated with a plurality of records stored in a database,wherein at least one record in the plurality of records includesincorrect data; generating a new cluster based on visited recordsrecorded during a breadth-first search (BFS) traversal over theplurality of records, wherein a size of the visited records is less thana maximum size; and writing the new cluster to the database by updatingeach of the visited records to identify the new cluster.
 2. The methodof claim 1, wherein the new cluster comprises a set of records in thevisited records associated with a single entity.
 3. The method of claim1, wherein the maximum size is equal to a pre-configured threshold. 4.The method of claim 1, further comprising limiting a depth of the BFS.5. The method of claim 1, wherein performing the BFS comprisesinitiating the BFS at a highest-connected record in the oversizedcluster.
 6. The method of claim 5, further comprising: ranking recordsin the oversized cluster based on respective connectivity of each of therecords; and selecting a highest-connected record based on the rankedrecords.
 7. The method of claim 6, further comprising: removing thevisited records from the oversized cluster and the ranked records;selecting a second highest-connected record from the ranked records;performing a second BFS on the oversized cluster, the BFS generating asecond list of visited records, terminating the second BFS upondetermining that a size of the second list of visited records exceedsthe maximum size, and generating a second new cluster from the secondlist of visited records.
 8. A non-transitory computer-readable storagemedium for tangibly storing computer program instructions capable ofbeing executed by a computer processor, the computer programinstructions defining steps of: receiving an oversized clusterassociated with a plurality of records stored in a database, wherein atleast one record in the plurality of records includes incorrect data;generating a new cluster based on visited records recorded during abreadth-first search (BFS) traversal over the plurality of records,wherein a size of the visited records is less than a maximum size; andwriting the new cluster to the database by updating each of the visitedrecords to identify the new cluster.
 9. The non-transitorycomputer-readable storage medium of claim 8, wherein the new clustercomprises a set of records in the visited records associated with asingle entity.
 10. The non-transitory computer-readable storage mediumof claim 8, wherein the maximum size is equal to a pre-configuredthreshold.
 11. The non-transitory computer-readable storage medium ofclaim 8, further comprising limiting a depth of the BFS.
 12. Thenon-transitory computer-readable storage medium of claim 8, whereinperforming the BFS comprises initiating the BFS at a highest-connectedrecord in the oversized cluster.
 13. The non-transitorycomputer-readable storage medium of claim 12, further comprising:ranking records in the oversized cluster based on respectiveconnectivity of each of the records; and selecting a highest-connectedrecord based on the ranked records.
 14. The non-transitorycomputer-readable storage medium of claim 13, further comprising:removing the visited records from the oversized cluster and the rankedrecords; selecting a second highest-connected record from the rankedrecords; performing a second BFS on the oversized cluster, the BFSgenerating a second list of visited records, terminating the second BFSupon determining that a size of the second list of visited recordsexceeds the maximum size, and generating a second new cluster from thesecond list of visited records.
 15. A device comprising: a processor;and a storage medium for tangibly storing thereon logic for execution bythe processor, the logic comprising instructions for: receiving anoversized cluster associated with a plurality of records stored in adatabase, wherein at least one record in the plurality of recordsincludes incorrect data, generating a new cluster based on visitedrecords recorded during a breadth-first search (BFS) traversal over theplurality of records, wherein a size of the visited records is less thana maximum size, and writing the new cluster to the database by updatingeach of the visited records to identify the new cluster.
 16. The deviceof claim 15, wherein the new cluster comprises a set of records in thevisited records associated with a single entity.
 17. The device of claim15, wherein the maximum size is equal to a pre-configured threshold. 18.The device of claim 15, further comprising limiting a depth of the BFS.19. The device of claim 15, wherein performing the BFS comprisesinitiating the BFS at a highest-connected record in the oversizedcluster.
 20. The device of claim 19, further comprising: ranking recordsin the oversized cluster based on respective connectivity of each of therecords; and selecting a highest-connected record based on the rankedrecords.